{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python for Finance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analyze Big Financial Data**\n",
    "\n",
    "O'Reilly (2014)\n",
    "\n",
    "Yves Hilpisch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img style=\"border:0px solid grey;\" src=\"http://hilpisch.com/python_for_finance.png\" alt=\"Python for Finance\" width=\"30%\" align=\"left\" border=\"0\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Buy the book ** |\n",
    "<a href='http://shop.oreilly.com/product/0636920032441.do' target='_blank'>O'Reilly</a> |\n",
    "<a href='http://www.amazon.com/Yves-Hilpisch/e/B00JCYHHJM' target='_blank'>Amazon</a>\n",
    "\n",
    "**All book codes & IPYNBs** |\n",
    "<a href=\"http://oreilly.quant-platform.com\">http://oreilly.quant-platform.com</a>\n",
    "\n",
    "**The Python Quants GmbH** | <a href='http://pythonquants.com' target='_blank'>www.pythonquants.com</a>\n",
    "\n",
    "**Contact us** | <a href='mailto:analytics@pythonquants.com'>analytics@pythonquants.com</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introductory Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implied Volatilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "uuid": "8e3ac03d-e5c1-4184-8494-5c02f5c1a897"
   },
   "outputs": [],
   "source": [
    "V0 = 17.6639"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "uuid": "3d727b16-4ff1-49fb-a54a-6e96568f54b0"
   },
   "outputs": [],
   "source": [
    "r = 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "uuid": "009042d8-f384-482c-8b4b-eeac6312315e"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "h5 = pd.HDFStore('./source/vstoxx_data_31032014.h5', 'r')\n",
    "futures_data = h5['futures_data']  # VSTOXX futures data\n",
    "options_data = h5['options_data']  # VSTOXX call option data\n",
    "h5.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "uuid": "8d5c855d-87d0-4108-93c7-57094610bd68"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>EXP_YEAR</th>\n",
       "      <th>EXP_MONTH</th>\n",
       "      <th>PRICE</th>\n",
       "      <th>MATURITY</th>\n",
       "      <th>TTM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>4</td>\n",
       "      <td>17.85</td>\n",
       "      <td>2014-04-18</td>\n",
       "      <td>0.049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>19.55</td>\n",
       "      <td>2014-05-16</td>\n",
       "      <td>0.126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "      <td>19.95</td>\n",
       "      <td>2014-06-20</td>\n",
       "      <td>0.222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "      <td>20.40</td>\n",
       "      <td>2014-07-18</td>\n",
       "      <td>0.299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>8</td>\n",
       "      <td>20.70</td>\n",
       "      <td>2014-08-15</td>\n",
       "      <td>0.375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>20.95</td>\n",
       "      <td>2014-09-19</td>\n",
       "      <td>0.471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>10</td>\n",
       "      <td>21.05</td>\n",
       "      <td>2014-10-17</td>\n",
       "      <td>0.548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>11</td>\n",
       "      <td>21.25</td>\n",
       "      <td>2014-11-21</td>\n",
       "      <td>0.644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          DATE  EXP_YEAR  EXP_MONTH  PRICE   MATURITY    TTM\n",
       "496 2014-03-31      2014          4  17.85 2014-04-18  0.049\n",
       "497 2014-03-31      2014          5  19.55 2014-05-16  0.126\n",
       "498 2014-03-31      2014          6  19.95 2014-06-20  0.222\n",
       "499 2014-03-31      2014          7  20.40 2014-07-18  0.299\n",
       "500 2014-03-31      2014          8  20.70 2014-08-15  0.375\n",
       "501 2014-03-31      2014          9  20.95 2014-09-19  0.471\n",
       "502 2014-03-31      2014         10  21.05 2014-10-17  0.548\n",
       "503 2014-03-31      2014         11  21.25 2014-11-21  0.644"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "futures_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "uuid": "48b29f70-8142-4960-8d4d-3a241685bc1d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 395 entries, 46170 to 46564\n",
      "Data columns (total 8 columns):\n",
      "DATE         395 non-null datetime64[ns]\n",
      "EXP_YEAR     395 non-null int64\n",
      "EXP_MONTH    395 non-null int64\n",
      "TYPE         395 non-null object\n",
      "STRIKE       395 non-null float64\n",
      "PRICE        395 non-null float64\n",
      "MATURITY     395 non-null datetime64[ns]\n",
      "TTM          395 non-null float64\n",
      "dtypes: datetime64[ns](2), float64(3), int64(2), object(1)\n",
      "memory usage: 27.8+ KB\n"
     ]
    }
   ],
   "source": [
    "options_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "uuid": "dead6085-55a7-46c1-9396-3ec0e247b8f4"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>MATURITY</th>\n",
       "      <th>TTM</th>\n",
       "      <th>STRIKE</th>\n",
       "      <th>PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46170</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014-04-18</td>\n",
       "      <td>0.049</td>\n",
       "      <td>1</td>\n",
       "      <td>16.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46171</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014-04-18</td>\n",
       "      <td>0.049</td>\n",
       "      <td>2</td>\n",
       "      <td>15.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46172</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014-04-18</td>\n",
       "      <td>0.049</td>\n",
       "      <td>3</td>\n",
       "      <td>14.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46173</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014-04-18</td>\n",
       "      <td>0.049</td>\n",
       "      <td>4</td>\n",
       "      <td>13.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46174</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014-04-18</td>\n",
       "      <td>0.049</td>\n",
       "      <td>5</td>\n",
       "      <td>12.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            DATE   MATURITY    TTM  STRIKE  PRICE\n",
       "46170 2014-03-31 2014-04-18  0.049       1  16.85\n",
       "46171 2014-03-31 2014-04-18  0.049       2  15.85\n",
       "46172 2014-03-31 2014-04-18  0.049       3  14.85\n",
       "46173 2014-03-31 2014-04-18  0.049       4  13.85\n",
       "46174 2014-03-31 2014-04-18  0.049       5  12.85"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "options_data[['DATE', 'MATURITY', 'TTM', 'STRIKE', 'PRICE']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "uuid": "808a0269-42d5-43ed-b753-28abb46820de"
   },
   "outputs": [],
   "source": [
    "options_data['IMP_VOL'] = 0.0\n",
    "  # new column for implied volatilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "uuid": "233c9f45-d3c4-48f4-8422-7e77c490d232"
   },
   "outputs": [],
   "source": [
    "from bsm_functions import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "uuid": "8da4e671-cbbc-44fd-8050-c8fe300a4501"
   },
   "outputs": [],
   "source": [
    "tol = 0.5  # tolerance level for moneyness\n",
    "for option in options_data.index:\n",
    "    # iterating over all option quotes\n",
    "    forward = futures_data[futures_data['MATURITY'] == \\\n",
    "                options_data.loc[option]['MATURITY']]['PRICE'].values[0]\n",
    "      # picking the right futures value\n",
    "    if (forward * (1 - tol) < options_data.loc[option]['STRIKE']\n",
    "                             < forward * (1 + tol)):\n",
    "        # only for options with moneyness within tolerance\n",
    "        imp_vol = bsm_call_imp_vol(\n",
    "                V0,  # VSTOXX value \n",
    "                options_data.loc[option]['STRIKE'],\n",
    "                options_data.loc[option]['TTM'],\n",
    "                r,   # short rate\n",
    "                options_data.loc[option]['PRICE'],\n",
    "                sigma_est=2.,  # estimate for implied volatility\n",
    "                it=100)\n",
    "        options_data['IMP_VOL'].loc[option] = imp_vol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "uuid": "94491814-c0c6-4565-b1f7-cedb1af12c48"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "496   2014-04-18\n",
       "497   2014-05-16\n",
       "498   2014-06-20\n",
       "499   2014-07-18\n",
       "500   2014-08-15\n",
       "501   2014-09-19\n",
       "502   2014-10-17\n",
       "503   2014-11-21\n",
       "Name: MATURITY, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "futures_data['MATURITY']\n",
    "  # select the column with name MATURITY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "uuid": "cbb44f1e-2ab5-4d8f-976a-2d36515eef12"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE         2014-03-31 00:00:00\n",
       "EXP_YEAR                    2014\n",
       "EXP_MONTH                      4\n",
       "TYPE                           C\n",
       "STRIKE                         1\n",
       "PRICE                      16.85\n",
       "MATURITY     2014-04-18 00:00:00\n",
       "TTM                        0.049\n",
       "IMP_VOL                        0\n",
       "Name: 46170, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "options_data.loc[46170]\n",
    "  # select data row for index 46170"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "uuid": "e267720a-901b-42fd-86eb-f31c29ebc166"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "options_data.loc[46170]['STRIKE']\n",
    "  # select only the value in column STRIKE\n",
    "  # for index 46170 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "uuid": "03092953-5496-4500-9cf5-e3cbcf46d396"
   },
   "outputs": [],
   "source": [
    "plot_data = options_data[options_data['IMP_VOL'] > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "uuid": "738b9618-b3a9-434f-a685-441a0c837b76"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Timestamp('2014-04-18 00:00:00'),\n",
       " Timestamp('2014-05-16 00:00:00'),\n",
       " Timestamp('2014-06-20 00:00:00'),\n",
       " Timestamp('2014-07-18 00:00:00'),\n",
       " Timestamp('2014-08-15 00:00:00'),\n",
       " Timestamp('2014-09-19 00:00:00'),\n",
       " Timestamp('2014-10-17 00:00:00'),\n",
       " Timestamp('2014-11-21 00:00:00')]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maturities = sorted(set(options_data['MATURITY']))\n",
    "maturities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "uuid": "8886807a-ca71-48d0-b5ef-ab7cb9470548"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4QgikZ+7JpGcumsSzdz3LOds5XjvwWlOHIoQQooI05gKo+7u5YT3DuKvXXaz5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9oAXTdYVz/2+RoPrK1xNdyKV9RfpyoW46tQiN3BIHnx7e5QiLxgkN3BIF+FQvzm0Ue55Y03JJiL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\nQnRx/w/+l1qwm7KSdgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5c797ea110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.figure(figsize=(8, 6))\n",
    "for maturity in maturities:\n",
    "    data = plot_data[options_data.MATURITY == maturity]\n",
    "      # select data for this maturity\n",
    "    plt.plot(data['STRIKE'], data['IMP_VOL'],\n",
    "             label=maturity.date(), lw=1.5)\n",
    "    plt.plot(data['STRIKE'], data['IMP_VOL'], 'r.')\n",
    "plt.grid(True) \n",
    "plt.xlabel('strike')\n",
    "plt.ylabel('implied volatility of volatility')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "# tag: vs_imp_vol\n",
    "# title: Implied volatilities (of volatility) for European call options on the VSTOXX on 31. March 2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "uuid": "34582055-b4b2-49ea-8678-e44288f7de88"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.DataFrameGroupBy object at 0x7f5c83874390>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keep = ['PRICE', 'IMP_VOL']\n",
    "group_data = plot_data.groupby(['MATURITY', 'STRIKE'])[keep]\n",
    "group_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "uuid": "1b5253de-0b31-4af0-8155-b2c405363a0d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>PRICE</th>\n",
       "      <th>IMP_VOL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MATURITY</th>\n",
       "      <th>STRIKE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2014-04-18</th>\n",
       "      <th>9</th>\n",
       "      <td>8.85</td>\n",
       "      <td>2.083388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7.85</td>\n",
       "      <td>1.804193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6.85</td>\n",
       "      <td>1.550283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5.85</td>\n",
       "      <td>1.316103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.85</td>\n",
       "      <td>1.097184</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   PRICE   IMP_VOL\n",
       "MATURITY   STRIKE                 \n",
       "2014-04-18 9        8.85  2.083388\n",
       "           10       7.85  1.804193\n",
       "           11       6.85  1.550283\n",
       "           12       5.85  1.316103\n",
       "           13       4.85  1.097184"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_data = group_data.sum()\n",
    "group_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "uuid": "390e4908-23ed-4419-a62d-b25ed3cf2d15"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FrozenList([[2014-04-18 00:00:00, 2014-05-16 00:00:00, 2014-06-20 00:00:00, 2014-07-18 00:00:00, 2014-08-15 00:00:00, 2014-09-19 00:00:00, 2014-10-17 00:00:00, 2014-11-21 00:00:00], [9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_data.index.levels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Monte Carlo Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "uuid": "13a3c945-2ae9-4583-b21f-cb6d4e4729b8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.0213522351431763"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from bsm_functions import bsm_call_value\n",
    "S0 = 100.\n",
    "K = 105.\n",
    "T = 1.0\n",
    "r = 0.05\n",
    "sigma = 0.2\n",
    "bsm_call_value(S0, K, T, r, sigma)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pure Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "uuid": "aebfedcc-472c-4225-82eb-6405ecf7eaa9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "European Option Value   7.999\n",
      "Duration in Seconds    16.190\n"
     ]
    }
   ],
   "source": [
    "%run mcs_pure_python.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "uuid": "d038ec31-373b-4979-aa49-a47ff96fc234"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.999"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum_val = 0.0\n",
    "for path in S:\n",
    "    # C-like iteration for comparison\n",
    "    sum_val += max(path[-1] - K, 0)\n",
    "C0 = exp(-r * T) * sum_val / I\n",
    "round(C0, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vectorization with NumPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "uuid": "2f695395-b7b7-4d0c-bab3-659fcd4da5d0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5]\n"
     ]
    }
   ],
   "source": [
    "v = range(1, 6)\n",
    "print v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "uuid": "548c6e07-9435-421c-9408-d63252608f74"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 1, 2, 3, 4, 5]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 * v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "uuid": "0a831df5-d2c3-46af-8399-ad2b4b411001"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "v = np.arange(1, 6)\n",
    "v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "uuid": "4c5153ff-983f-4ee1-95d6-0dae809da4bf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  4,  6,  8, 10])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 * v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "uuid": "0fca4476-2003-4733-a6e1-f405997e1b1a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "European Option Value   8.037\n",
      "Duration in Seconds     0.795\n"
     ]
    }
   ],
   "source": [
    "%run mcs_vector_numpy.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "uuid": "6d10a22b-fed3-4627-a2bc-6f958824ca2f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.35"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(tpy / tnp1, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Full Vectorization with Log Euler Scheme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "uuid": "02a121e5-75ff-4ad1-9812-ec0e69d3ebaa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "European Option Value   8.166\n",
      "Duration in Seconds     0.850\n"
     ]
    }
   ],
   "source": [
    "%run mcs_full_vector_numpy.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graphical Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "uuid": "f880bd6e-72fa-4906-b2a7-24f3c7419338"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f5c74f33710>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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g9bbW2Ht/L16rNw6/uroW+pXSsOEkGBp+DlfXsVCryy5D++nTpzF+/Pgya+9F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\nmHr+H5wk53khlMRQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5c74de0850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(S[:, :10])\n",
    "plt.grid(True)\n",
    "plt.xlabel('time step')\n",
    "plt.ylabel('index level')\n",
    "# tag: index_paths\n",
    "# title: The first 10 simulated index level paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "uuid": "997e2c48-f4e4-49c2-be9b-ce2ed0a2166f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f5c74d15e10>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5c74de0650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(S[-1], bins=50)\n",
    "plt.grid(True)\n",
    "plt.xlabel('index level')\n",
    "plt.ylabel('frequency')\n",
    "# tag: index_histo\n",
    "# title: Histogram of all simulated end of period index level values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "uuid": "bc4f9174-c92c-4abf-9d31-da3e7d0baec5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 50000)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5BcvZNCeQ3CDpxrLt1PSFnHbxXLdL+rNyMaXrJT2pT47H3xWUs8ueL+lmSV+R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\nIiJal+ISERGt+/84HKWzpk+rJgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5c73adb190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.maximum(S[-1] - K, 0), bins=50)\n",
    "plt.grid(True)\n",
    "plt.xlabel('option inner value')\n",
    "plt.ylabel('frequency')\n",
    "plt.ylim(0, 50000)\n",
    "# tag: option_iv_hist\n",
    "# title: Histogram of all simulated end of period option inner values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "uuid": "d5aed456-001a-423c-9366-df294f1c3f0b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "133533"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(S[-1] < K)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Technical Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "uuid": "dcba1a61-94e3-47dd-ab54-2c688e740741"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pandas.io.data as web"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "uuid": "3b6ce702-6a8c-400c-a6fc-d382f316dd9c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 3592 entries, 2000-01-03 to 2014-04-14\n",
      "Data columns (total 6 columns):\n",
      "Open         3592 non-null float64\n",
      "High         3592 non-null float64\n",
      "Low          3592 non-null float64\n",
      "Close        3592 non-null float64\n",
      "Volume       3592 non-null int64\n",
      "Adj Close    3592 non-null float64\n",
      "dtypes: float64(5), int64(1)\n",
      "memory usage: 196.4 KB\n"
     ]
    }
   ],
   "source": [
    "sp500 = web.DataReader('^GSPC', data_source='yahoo',\n",
    "                       start='1/1/2000', end='4/14/2014')\n",
    "sp500.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "uuid": "444a17d9-8fd1-436d-825a-3001cb080b7c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5c74de0810>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BbMixiDWCNmuSimUEXXAK+oADgB49vG3GQOKLwtwY2/a++wKHHBJ+bPNmsVGY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\nLRftAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5c6d593150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sp500['Close'].plot(grid=True, figsize=(8, 5))\n",
    "# tag: sp500\n",
    "# title: Historical levels of the S&P 500 index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "uuid": "d134fa6d-4db4-40f3-b1ce-952ef2346e18"
   },
   "outputs": [],
   "source": [
    "sp500['42d'] = np.round(pd.rolling_mean(sp500['Close'], window=42), 2)\n",
    "sp500['252d'] = np.round(pd.rolling_mean(sp500['Close'], window=252), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "uuid": "8af03824-0abb-4747-9f8f-d517d48bd673"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>42d</th>\n",
       "      <th>252d</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-04-08</th>\n",
       "      <td>1851.959961</td>\n",
       "      <td>1853.88</td>\n",
       "      <td>1728.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-09</th>\n",
       "      <td>1872.180054</td>\n",
       "      <td>1855.67</td>\n",
       "      <td>1729.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-10</th>\n",
       "      <td>1833.079956</td>\n",
       "      <td>1856.46</td>\n",
       "      <td>1730.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-11</th>\n",
       "      <td>1815.689941</td>\n",
       "      <td>1856.36</td>\n",
       "      <td>1731.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-14</th>\n",
       "      <td>1830.609985</td>\n",
       "      <td>1856.63</td>\n",
       "      <td>1732.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Close      42d     252d\n",
       "Date                                     \n",
       "2014-04-08  1851.959961  1853.88  1728.66\n",
       "2014-04-09  1872.180054  1855.67  1729.79\n",
       "2014-04-10  1833.079956  1856.46  1730.74\n",
       "2014-04-11  1815.689941  1856.36  1731.64\n",
       "2014-04-14  1830.609985  1856.63  1732.74"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp500[['Close', '42d', '252d']].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "uuid": "4dc6ac7e-6fac-4994-811b-35789c551de7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5c74efc810>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vsphnAp7BK4+dOWk//RTefz/DvbaTkiMUdGysaTsqKuW6y5er9/ffN5VlIz84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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5c74feb290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sp500[['Close', '42d', '252d']].plot(grid=True, figsize=(8, 5))\n",
    "# tag: sp500_trend\n",
    "# title: The S&P 500 index with 42d and 252d trend lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "uuid": "ccc2b0e9-5ab7-49c1-8054-3a1e1b23ab92"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2014-04-08    125.22\n",
       "2014-04-09    125.88\n",
       "2014-04-10    125.72\n",
       "2014-04-11    124.72\n",
       "2014-04-14    123.89\n",
       "Name: 42-252, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp500['42-252'] = sp500['42d'] - sp500['252d']\n",
    "sp500['42-252'].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "uuid": "e127f7c9-8e7f-4038-b490-502e6bf436f7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2000-01-03   NaN\n",
       "2000-01-04   NaN\n",
       "2000-01-05   NaN\n",
       "2000-01-06   NaN\n",
       "2000-01-07   NaN\n",
       "Name: 42-252, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp500['42-252'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "uuid": "d1125e1b-3cd0-4274-aee7-9b1136db8c02"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1    1489\n",
       " 0    1232\n",
       "-1     871\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SD = 50\n",
    "sp500['Regime'] = np.where(sp500['42-252'] > SD, 1, 0)\n",
    "sp500['Regime'] = np.where(sp500['42-252'] < -SD, -1, sp500['Regime'])\n",
    "sp500['Regime'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "uuid": "ffec4dc1-ebb4-43e1-962d-1fbc465afee1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.1, 1.1)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HS9qLeoaqxcVG1nEPFS1jab60qIVxL5J+3DIh+nVs49EyTlFU3riL2Ilz93zu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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5c6d5fd150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sp500['Regime'].plot(lw=1.5, grid=True)\n",
    "plt.ylim([-1.1, 1.1])\n",
    "# tag: sp500_signal\n",
    "# title: Signal regimes over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "uuid": "0e51676f-7b84-441d-8191-08799247774c"
   },
   "outputs": [],
   "source": [
    "sp500['Market'] = np.log(sp500['Close'] / sp500['Close'].shift(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "uuid": "25f3c42a-ec95-46e3-a194-4e29f0c830af"
   },
   "outputs": [],
   "source": [
    "sp500['Strategy'] = sp500['Regime'].shift(1) * sp500['Market']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "uuid": "ce759e67-918d-432c-a411-b22c6777dac5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5c6d66f190>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ex6OP0+OHHlyKu0SHqh0IDgimTFAZrrzjmjjarka1fBUKhdcghGDNiTVWxlef\ng7ry6EqH5214doNp/8DVAy6RpXSx0oxtN5blT0q/5HNNnjMdaxvRlpk9Zto9LyElwSXXdxdP3v0k\nSx5fwsr+K+1uv/SVc5jrl6lPWJEwAMQ4wdBmQzl8/TAgXzpqfVHLbv16SzXxvUTTffvpwE9EfB7h\nVK7237XnyI0jFPMv5pKejvzGEMbXCPMe7WFUvUHpXlg5H3ueLvO7UPv/apvyriVckzunHJ9n+Wds\n2WWdG8oFl+O7R77j6n+uEnl/pCm/Sfkmpv26peo6nE606/Iu0/7gpoPzJItOft/zIU2HmPYjikfw\nWL3HqFWylt2tdil5b4L8g7irxF2kjJZdzbVL1jaNfL4Qe8EqGhjIuc9apMa+K/sY0HAARf2L0iai\njcmYg4ywNfzP4abR7rreHb7rwMW4i4C8P96AIYyvQqEoHCSk2rYadZ9vSGCI1XqtlliObm0+q3mO\nr3vr9i3TfpB/EK0qOx9I9MP+Hxwes4wvnJvub3fQspJ5mb2JD0x0UhKK+hUFzEEt/H3lqOzKxStz\nLlYuTr/78m7A2n2w5sQaACZsmGC19rJlKMuBSwfy3//9l9d/f53Rf4/m253fMvrv0aw/s95UxlOW\nO8wKQxhfI/jA7GFUvUHpXlixF9YwKTWJNSfWULd5XXw127mggN05otnhSvwVtEiNCf/IZeYCfAO4\nHH/Z1J2ambYRbQHnLdqGZRua9l015SW/77ll8BAfzbnZqBZejYT3bF+SKoVW4lzsOdafXs/JmycB\n2ZOh8+yyZ037ljGmn2r4FAdePkAx/2IsPbwUgLLBZSniV4TazWrbfIeZF0/wVAxhfBUKReHAnvH9\naNNHdJnfhdtpt50as371+zk85gg9WMSnmz/l5WYvk5KeQkJqAsUDi9str1+jW41uDuvsU68PEzvK\n1qOnLFCQFbrxndp1arbK24u+VTm0MqdunqLD9x0Yv348gGlU87WEa6RlpBEzIoaVT65kab+lpvP8\nfPyoWaImyWnJBAcE82X3LxndbrRpG99hPFffucrpYacBbNbo9VQMYXwLsw/MGUbVG5TuhRV7xldf\nDOCZKc+w7+o+AMoGlbUp17d+XwCHrWN7tK/a3rRvufaroylCTzV8CsBmVG5mRrYdCcCsnbOyLYsz\n8vuet6jYgoohFXM8J9mSMkFluHn7plXeU788xf3f30+ZT8qQlpFGaGAoD9V6yCYOs5+PH+kinfSM\ndFPkMjAHvXRCAAAgAElEQVTrXTqoNFXCqnDhrQvM6uma7zS/MYTxVSgUhQNnq+n4+/rz5N1P0rhc\nYy6/c9nmeO+6vVnVf1WOVrOxHJls2dXsKEh/WJEwFj62kOrh1bNVf+2StbMu5AG0rtya82+dz7qg\nE+x1/Xeu3pmx7cYSEhDidA6w/n0npSU57HUAqBBSwSMDatjDEMa3MPvAnGFUvUHpXljJPG/U6lhE\nKm0i2rBr8C6HZRqXa0z54PJ8vuXzbMX2/WbXN6Z9y3nDznji7iey5ctd8vgSogZFZavOrPDGe37i\n9RP8MeAP7q92P43KNcqyfJuINoD1i4836q1jCOOrUCgKB5brzdrD0UAonSD/IC7FX+LN1W8yZOUQ\np2VBxmh+ptEz2ao7pzxW7zGvmRbjagJ8A6geXt00eOu1Fq/x5N1POj2ndLGCXRUpv3FqfDVNq6xp\n2jpN0w5omrZf0zS7Hf6apk3TNO2Ypml7NE1rYq+MOynMPjBnGFVvULoXVpx2GZ+SoQ+dYdklOWvn\nLNIz0ll/ej2X4227qUHOGf2w44eA9Fm+eM+LvNbitZwLns94yz3/9uFvAdtQoH3r9+WHxxxPzwL7\n99Zb9LZHVn0jqcCbQojdmqYFAzs0TftTCHFIL6BpWneghhCipqZpLYGvgHvzT2SFQmFEriVcc2gk\ndbKaN5u5O3jnpZ10+L4DgM0c4SD/IKtpRSWLlmRmT/tRqxTZ47kmz/H88udztZzfF92+YHyH8a4X\nyk04Nb5CiMvA5Tv78ZqmHQIqAIcsij0MfH+nzFZN08I0TSsrhPCYgJre7BfIC0bVG5TuhZFGXzfi\nUvwlxwWqOR6F7AhLI/Dpg59Ss0RNU7rTPLmAQTH/YjQp14Rq4Z47hcWb7vm7972bq+UQQwJDCAkM\nscrzJr0zk+0Z3pqmVQWaAFszHaoInLNInwcqAR5jfBUKhfdjz/CWLlaaa4nXTEvHXYi9kKM6LY1v\nzRI1TaERAW4myWkxmqaxc/DOXEqtyMxHnT5ytwgeQbYGXN3pcl4CDBNCxNsrkimdt+CpLsab/QJ5\nwah6g9LdCPSu25t5j84D7gRWOAV1StXJUR16rGEwh0HUsRfK0lMxyj3PjDfrnWXLV9M0f+BnYL4Q\nYqmdIhcAyxnlle7k2TBo0CCqVq0KQFhYGI0bNzZ1G5gCZKu0y9K7d+/2KHkKMr17926PkkelXZA+\nBeg9v6egZ6OedKnRhWOvHWP64ukcuXyESqGVslUfp6BRuUZcTbhqSm/btI3qPapbXU+rpnmO/k7S\nRn3edTxJnqioKE6fPk1WaPrqEHYPyglV3wM3hBBvOijTHXhVCNFd07R7gc+FEDYDrjRNE86upVAo\nFM7QIq072K6+c5XSQXL6yfy983n616eJHh5NeNHwbNXzSO1HiCgewRfbvgDg7BtnrSJTaZEafj5+\npI5xPLdYoXCGpmkIIexGZMmq5XsfMADYq2maPnP9PSACQAgxQwjxm6Zp3TVNOw4kAM/ar0qhUChc\nh2UA/XMxcthJdoJb1ClVh2L+xSjmX4xVx1bRNqIt2y9utwkJObrtaJuuaIXCVTht+br0Qm5s+UZF\nRZm7mwyEUfUGpXth1D1zy1eMM/+fHLtxjFpv1yJxViJF/Ys6rSc+JR4fzYegiXLO77ONn2X2I7Nd\nL3ABUljveVZ4ut7OWr4qwpVCofA6Mk9VqVmyJiv6r8jS8AIEBwRbrbozZ/ccl8unUGSFIVq+CoXC\n+7Fs+YYXCSf63eg81ffB+g8YGzWW1pVbs+m5TXkVT6GwwVnLVxlfhULhFVga35CAEGJH5n0t3MTU\nRHw0nywjYykUucHw3c6Zh6UbBaPqDUr3wo69BRZyo3cx/2KFwvAa4Z7bw5v1NoTxVSgUhYuktCR3\ni6BQ5AnV7axQKLwCZ6OdFQpPxPDdzgqFQqFQeBKGML7e7BfIC0bVG5TuRsSoeoNxdfdmvQ1hfBUK\nReGhRNES7hZBocgzyuerUCi8At3n26l6J/46+Zfy+So8HuXzVSgUhQbNZgVThcL7MITx9Wa/QF4w\nqt6gdC+MNK/QnA87fsjLzV9mcNPBNscLq97Zwai6e7PeWS8BolAoFB5AoF8gbSPa0rZKW3rV6eVu\ncRSKPKF8vgqFwito9W0rPn3wU1pXbu1uURSKbKF8vgqFwutJz0jP1nq9CoU3YAjj681+gbxgVL1B\n6V4YSctIw1fzdXi8sOqdHYyquzfrbQjjq1AovJ90oVq+isKD8vkqFAqvoP70+izqs4i7y9ztblEU\nimyhfL4KhcLrSc9Id9rtrFB4E4Ywvt7sF8gLRtUblO6FkcTURIICghweL6x6Zwej6u7NehvC+CoU\nCu8nPiWe4IBgd4uhULgE5fNVKBReQcAHAcSNjCPQL9DdoigU2UL5fBUKhVeTkp6CQBDgG+BuURQK\nl2AI4+vNfoG8YFS9Qele2EhISSA4IBhNc7yoQmHUO7sYVXdv1tsQxlehUHg3l+IvEZcc524xFAqX\noXy+CoXC4yn939JcT7yu1vBVeBXK56tQKLya64nX3S2CQuFSDGF8vdkvkBeMqjco3Y2IUfUG4+ru\nzXobwvgqFAqFQuFJKJ+vQqHweLRI6TZTPl+FN6F8vgqFwmu5mXTT3SIoFC7HEMbXm/0CecGoeoPS\nvTDRdk7bbJUrbHrnBKPq7s16G8L4KhSK7HEt4Rpzds3Bk1xE52PPu1sEhcLlZOnz1TRtNvAQcFUI\n0cDO8Q7AMuDknayfhRAT7JRTPl+FwgO5EHuBCiEV0DSNN/54g6lbp7LthW00r9jc3aIBEPZRGDHJ\nMXz78Lc81+Q5d4ujUGSbvPp85wBdsyizXgjR5M5mY3gVCoVncuv2LSpNqYTv+75M3z6dqVunAjBx\n40Q3S2YmJjkGgDO3zrhZEoXCdWRpfIUQG4CsRjw4DrjqAXizXyAvGFVvULpnhwNXDxD+cTgAAsEr\nv71iOta8gme0ei25knDF6XF1z42HN+vtCp+vAFprmrZH07TfNE2r54I6FdlACEF8Sry7xVB4Ifuv\n7ufur+62eyzQN9Aj4yj7+fi5WwSFwmVka56vpmlVgRUOfL4hQLoQIlHTtG7AVCFELTvllM/XxSw+\nsJi+S/qquY+KHBGXHEe96fU4H3ue+qXrc+DaAdOxKV2mAPDbsd+oFFqJ2Y/MLlDZtEiN+JHxBAUE\nWeUBLH58MX3q9SlQeRSKvODM55vnV0khRJzF/u+apk3XNK2EECI6c9lBgwZRtWpVAMLCwmjcuDEd\nOnQAzN0HKp399LhV4yAYj5HHXnqb/zZik2P5cO6HAITXDSf63WhWrVlFEb8iPNDxAY+S1wjpcVHj\nOL9HjiDeMnIL7619jy8WfUHvur0Z3HQwP+7/kT/X/gnAx50+pnRQaavzb6fdpugLRVk3aJ1L5UvP\nSAcgPiWe7f/bDkBEowgAOAWlrpaCO/1qnvR9qrRK62l9//Tp02SFK1q+ZZEjoYWmaS2An4QQVe2U\nc1vLNyoqyvQlFSayivrjbr3jU+IJmRRiky/GCbRIjeCAYKKHR+Pv6+/ya7tbd3eSle76cxM9PJrw\nouE2x3868BP9lvSzyru/6v2m/XWn1wGQMTbD6fq6OhvObKBphaYU8y/mtNzBawepP70+AEdfPUrN\nkjV5ZdUrTP93OpB1dCt1zzu4W4wCx9P1ztNoZ03TFgL/A2prmnZO07TnNE0brGna4DtF+gD7NE3b\nDXwOPOEqwRWSylMqcyPxhkvqEkKw4+IOl9SVFSP/GmmT5+/jT1JqEiCNc8CEAPZd2Vcg8hy5fqRA\nruNJfPK/Tzgbc9aUTk5LBuD0sNN2DS/Iub6ZGd1utGlb8eQKAPZc2ZMtGdp9146giUFZlnvql6dM\n+wv2LQAwGV6ForChYjt7AVqkxu7Bu2lUrpFVfr0v63Ho+qEc+Xx3XNxBs1nN8t1PvOzwMnot6mVK\nr3xyJd1qdqPMf8vwc9+fefjHh4lNjgXg136/0qtOL7Zd2EaAbwDFA4vn6dqaplGleBWrVtnZmLNU\n+bwKR149Qq2SNkMS3MKsHbO4FH+Jse3HurzuG4k3aDunLYeuH6JnrZ4sf3I5AGP+HsOEDROc3v+b\nSTcpMbmEVV7m8tmNtZyclkyRD4tkq6xep+U1LfPU2AaFt5GvPl9FwRDgG2CTl5uRzvo5QohsdRnm\nll2XdwGwoPcCAn0DefCuB/HRfLiRdIMO33egTUQbfDVf1p9Zz6OLHuXgywdp+U1LAKqGVc3TtS/E\nXmBy58m8ce8bZIgM/rPmP3y25TMAvtv9HRMfcM8c1rSMNNIy0lh9fDWpGam8tPIlAJcb38vxlyn/\naXlT+nj0cZJSkxiyaghz98zN8vzMLWJ/H/tugdDA0CzrupGUvR6bmNsxNnmWhjdupOeNvlYo8oIh\njK+n+wWcofcWZPaL/n3qb87FnnN6rqXeP+7/kV8O/ULHah0B5KAZ/6KuFxgpc+T6SCZ2nEj/Bv3t\nlgkrEsb8R+cT9nEYII11eJFwdry0g2rh1fJ0/Z8P/swnCz+hVaVWzNgxgzm755iOTdo4yW3G1/8D\n1/u2dVYeXUnPhT158943mbJwClSDCiEVeKX5K8zeNZudl3aaDO/Nd7NeqOD9Du8zNkq+FOgjoC35\n6qGvmL1rdpYvcZYviH+f+tv0/GXmr5N/OaxjbLuxBAcEZymzN//O84pRdfdmvQ1hfL2Z5HTpo8sQ\nGVb5D8x9IEf1fLn9Szae3cjig4sBSExNzDfj++/FfwEYdu8wm2N1S9Xl0PVDVAurRvEixdk1eBdN\nZjRh8cHF9K7bO8+GFyCieARbzm3h3m/vtcpf2m8pvRb14tC1Q/Rc2JPUjNQ8X8se6RnpdKjagfm9\n5xNzO8b0gpGZHS/toOnMpmw9v5WWlVrm+npHrh+h58KeAEzZIg1lmaAyzHt0HtXDqzN9+3QSUhMA\nqFWyFmFF7MtjyZj2YxgbNZbG5RrzSotXbI4HBwSz/eJ29l/dT4OyNuMw2XFxB5VCK3Hy5klT3gNz\nH+D0sNNEFI+wMdg/7P8BgJ0v7eSemfdYHRvVblSW8ioU3oYhjK+3vhkBpsFJOy7uoFpYtRyNDLbU\ne+PZjVbHElITKElJl8iYmRbftKBpefujWw++chAtUqNccDkAGpdrDMDSw0v5b+f/uuT6VcKqgIUN\n3ztkLw/Of5CHaj1EgG8A9abXo0m5Jvza71eXXC8zh68fpuuCrizYt4CyQWWtjl1++zIj145k7p65\n3FP+HmqVrMW+q/tybXx7LuzJyqMrAfiw44cE+gZSr3Q9utXsBsDJmye5EHeBLvO70Ld+Xxb1WZTt\nurvX7G41ytmSkAA5iv3FFS/SNsJ21aFPNn9i97yqU6uy6blNtK7c2iq/cmhlPnvwM5qUb0LG2Ax8\n3jePBbXncrGHN//O84pRdfdmvQ1hfL2ZpDRpfPv/0p+ktCS7geWjk6IpUbSETb4jigcWlz62vI1r\nsovezbjxuY0Oy1x6+xLhRcx+xWEthzF161SGNhvqEhnKBJXh+GvHqfFFDQDuLnM3l96+BEDDsg35\n9+K/vNT0JWmk8wHLeq8kXKFPvT4sObgEfx9/ygaXZfYjs03BK1pXbs2uS7tyfI2fDvxE+eDyJsM7\nrv043mv7nk25yqGVTfunbp7K0TVW9V/l8FipYqUAOaCkTFAZm+N1StXh8PXDAHz3yHcMWjbIdMze\nyP2ElARTYA1N0+hRqwedqnXi7jL2o3ApFN6OIYyvN/sF9JYvYApCYImv5suxG8fstpx0vTOPIm1c\nrjE7Lu0gLSONJuWbuEzWYzeOUev/5EjiIn5FHJbTW706n3f9nM+7fu4yOQDO7T3H6LajCQoIsuri\n/N9z/6PHwh4MaTbEpdfLTLsq7fjnzD8ARIRGkDw62e4yfbsu7WLPlT02boWs+HrH11bpN+9907Rv\n+bxb9pS8cM8LObqGM/Qei/899z+7Pt8+9fpQfVp1QPqehzQdYpJZH+UOcmT122veZt3pddxfzdzK\n1qcz5QRv/p3nFaPq7s16G8L45hc3Em9Qslj+dN3q6C1fgEC/QJvjzSs2J13YGmVH7Bu6j/FR43l2\n2bOAa6ZvHI8+TqBvoMnwegofdPzAJs/f15/VA1bn+7XXDFhDukjn/u/v59Munzost/WFrUzeNNnU\nkswu3Wp04/fjv7P1ha20qNjCadkZPWYQlxzHi/e8mKNrOCMoIMhpkA3LEdOBfoF81eMrk/HV5x3f\nTrvNXyf/Ys7uOcx/dD4P1XzIZfIpFJ6OmuebS9Iz0vH7wI/UMan5GvB9+4XttPhG/rn+0PsHnmzw\nJCnpKQROCGT7i9tpPqs5AxoOYN6j8+yeb2+e5ZCVQ5ixYwaQ/ShF9rgcf5mElART967O+kHraVel\nXa7qVGQPIQTz9s5jYKOB7hbFIfpzumfIHhqWbci0rdMYuXYkiamJHHj5ACPXjmT5ETn/WM3hVRRG\n1DzffOB22m1Azk/Mz9avZcs3JT0FkCOViwcWp1mFZgDM3zvfofGNS5HzI5c/sdyUZzlto9jEYiSN\nSrI5zxm6f85yLqlOXoy5IvtomubRhhfkQKlTw05Rpbj0gb/e8nXikuMYvW60KYykQmFUsgwvWRiw\nDHrtKnTje+v2LZfXbYmlzzcpLYnL8ZcZ8dcIm5HEvx76lXHrxpnS8/bMo8fEHqbBLT1r9zQds/TH\n6npkl0txlwieFMzrv79uyrNc+9VTDG9+3HNvwZN0rxpW1eqZGNVuFPVL54/h9SS9Cxqj6u7NequW\nby7RjdbN21kHLMgLli3foauGMnnTZE7dOmUz/aL3T70BiLw/kktxlxi4dCCcgoitETZ1Bvra+o6z\nQ3pGOhU+qwDAF9u+oExQGa4mXKVOqTqMaz+Oa4m2MYEVisx82f1LOnzfAYC/nv6LNhFt3CuQQuEG\nDNHyzY/RcHrwi9xME8kJli1fMHc9658fdvzQ6nhaRprJQFINqoXZBq3QZd8zRAbG33BmQ7ZkyRxI\nf3Tb0YhxgrmPzuWhWg8xqPGgbNVTEHjrCEhX4Om6t6/ann1D91G6WGlqlaxldyBhbvB0vfMTo+ru\nzXqrlm8u0Vu+Y9aN4cWmrhtFmpkfD/xolX6m0TNM3DiRd1q9A2CzSMCkDZOs0idvnuTzLtbTeJ5q\n8BQp6Sk0LNsQkKvOWEY90tBY1GcRM3bM4POun1MptBIASw4usarH2XQihcIZd5e5m6v/uepuMRSK\nfOH6ddi503kZQ7R888MvEJ0UDeCywBCO0EeD6uhhAvXu7sfqPsbhVw6bjuvxeAE4JeeD3lXiLqs6\n6pauy+TOkwGoHi7nYp58/aRpa16xOYsPLubnQz9TeUplZu2YxZCVQ5i0cRLf9/qeAy8fADAFUfBE\nvNkXlFeMqrtR9Qbj6u5pek+dCl27Qps20KWL87Kq5ZtLLsXJiEn57edsWr4pXWt0JdA3kLFRYzkb\nc5bIDpG81uI1QA5wql2qts15O1/ayT0jZYxcZ9GvfDT5/mU5L7NiSEVm7ZxlSuur7wB0rNaR8sHl\n6VitI2+3fjtvyikUCoWXos+c1ccTxsTAG29k/3xDtHzzwy9wMe4iIBcsyC9S01PZcWkHj9R+hDHt\nxwCytdm5emeHC6GDDDXYpHwTU3xjZ8Z39YDVJt+vTs9aPe2WPfTKISqFVsLXx5e1A9dSIaRCDjUq\nOLzZF5RXjKq7UfUG4+ruLr1nzgQfH7n16AFXr0JY1uuVWGEI45sfXIy7yKvNX8VH8+FK/JV8uYbu\nV25aoakp79D1Q3ajIYlxwhTsQ4+1O7PHTKu0PaqHVzf5fnW61JD9JcufWM78R+eb8uuUqpMbNRQK\nhaLQkJ4Okyeb01u3whdfyP0VK2Q6IwPCHbePAIMY3/zwC1yKv0Td0nXJEBk0ntGYFrNaMPzP4S69\nxtmYs4QGhpq6hnUcBfW49e4tOlfvzFMNngKgZlxNxDiRo0UXAIr5F0OME/Ss3ZOnGj7FiPtG8HYr\n7+pi9jRfUEFiVN2NqjcYV/eC1HvtWmjZEkqVghMnZGv34YehXDk4dgxGjJCt4BYtZFf0f/7jvD7l\n880lF+MuUqOEDKt4Of4yl+Mvk5aR5tJr3P2V/RVdHK3HGhQQxJqn17hUBoBJnSZlXUihUCgKKadO\nQadO5rS/P5QuDVOmwF13wf79sG2b9TkjR8J7tguNmTBEy9fVfoH4lHjWnlpLxZCKTLh/gik/vxan\n13mhiVyVJnNL2BFG9QOB0t2IGFVvMK7uBaH3kiVQXU4K4ZM7y1QHydUvqVrVXK5Zs5zVawjj62q2\nnN8CQMXQigjMAeHtLfmXW/R1cS2Z0nUKx1475rJrKBQKhcIx16/D44/L/R494O23Zd6NO0tS+/jA\ngAFyP6eRdQ1hfJ35BZ5f9rxNYIqsEELQqlIrwoqE8WzjZ00Lfutzf13BrB2zbPKCA4JNXd3Zwah+\nIFC6GxGj6g3G1T2/9R41Sn42agSf3lkZtGRJaXR1OnbMXd2GML6OWLB3AbN3z+abXd/k6LyE1ARK\nB5UGZOt339B9bH1hK8eij7H3yl4A1pxYk6eW8Ftr3gKgdknbObwKhUKhyB9iYmQrNj0dkpPhm29g\n926o5WC58kGDICUl59cx9Hq+WqS5nyAn64k2+roR1cKqsfSJpaa8DJGB7/u+vHTPS+y4tIMdl3bw\nW//f6FazW47lEkLQ4KsGPFDtAT7v+rnHrBSkUCgUhQkhYMcOaN4cNmyQkan274cGDWDSJDloato0\neO213NXvbD1fQ7d8c8veK3vpWM26r8FH82F46+HM3DmTHZd2APDEz0/kqv5Rf4/iwLUD9KrTSxle\nhUKhyCdeflkaXoC2beXn6NHyc+RI+dmnT/5c2xDG155f4FzMuVzVdTNJxlS2F9P56UZPW6Vjk2MJ\n+yiHYU+ASRulDzo0MDQXEpoxqh8IlO5GxKh6g3F1z4nen3wipwbpHbAZGfD11/Doo7L1C7J7+Z9/\nrKcNlS/vOnktMew837Wn1lqlhRBZtjKFEJSYLANW+Pv62xy/u8zdRHaIZFzUOHw0HzJEBjHJMZT9\npGyOZPP38Sc1I9XuNRQKhUKRc/SgF4sXQ2gofPWVDJDxyy/mMk2aSH9vs2YwfTq8/37+yWNYn2/d\nL+tarcqzf+h+6pepb1MuQ2Sw+dxm2sxpw+W3L1Pu03KAcx/x67+/TuXQygxuNpjiHxVn75C9TkM8\n2mPk2pFM6zaN4IDgHJ2nUCgUCmsWLDBPCdL5v/+D5cth9WqZbtdO+n07dpTRrISQm08e+oed+XwN\na3z1wVZtI9qy4ewGXmjyArMetp3e8581/+GTzZ9Y5ZUoWoIbw29k6zrZaVErFAqFIv8IDoaEBPj1\nV9nNrDN9Ogy18CC++64ME5lVXObsYvgBV878Av88+w9Tu061uzD87bTbNoZ3TLsxXP/P9Wxf252G\n16h+IFC6GxGj6g3G1T0rvffvlwExEhLg0iXo3Nm6JZt5MNXHH7vO8GaFIYyvPfrV78cPvX8AoKhf\nUf5v+/+x57J5ab3fjv1G0Q9tw0WObDNStWQVCoXCC2jQQC6EAFC2rAwLmW4RfqF0affIBQYxvpnj\nf6ZlpHE+9rxpNHGAbwAAVxOumsrsv7rftN82oi2zH57NoMaD8j1+sysxarxXULrnBxkZcoDKvn35\nUn2eUffceNjTOzoa+vWzDvc4aZJ1eupUc/Qqd2HI0c5d5ndh07lNJuN7I0n6bzef30zpoNKUCy5n\nZYh/evwnygWX49kmz7pFXoUiP7l8WbYKNA1mzIAhQ+DsWahc2brc88/DlSvQtStcvGiesqFQeBKd\nO8POndZ5w4ZZp19/veDkcUSWLV9N02ZrmnZF0zSH77uapk3TNO2Ypml7NE1rkhMBFiyA06dzcobk\n9u3sl7X0C8Qlx/H3qb8BuW4tQJC/XKJiXNQ4msxoQvlPy/Pp5k+Z1nUaYpygXHC5nAvoARjVDwRK\n9+yQkSGnWZQvL/1gL70kDS/ABx9Yl/3kE/juO7l/8aL8TE11ibguQ91z42Gp9/798Oqrcvm/BQvg\n3Dm4dk0u61fUAzsss9PtPAfo6uigpmndgRpCiJrAS8BXORFgwACoVs0cVcQeiYnw4Yfm+JkzZ8ov\n88CBnFxJcjHuomm/VDHpDHix6Yss6rPIpmyfevkU2kShcDNXroCvLzz2mDlvlsVg/8yBBfQ5kidP\nmvPuu09+1qgBi2x/PgpFgSAEVKwo/btffinX2u3fHypVkv7eDz90t4T2ydZUI03TqgIrhBAN7Bz7\nGlgnhFh0J30YaC+EuJKpnN2pRpb98JkPCyEN77590KqVDHBdrpxc2snROZmZvWs2F+MuMrqdtO6r\nj69mzLoxbH1hq83AqbjkOEI/MkeVykm8Z4XCW7h2Dco4mHY+aBBUqQJ//y1fcFevhl275PSLN96Q\ncyGD70w9L18eLlyQrebwcNkt/d//FpgaCgUA8+fD0xbBBRs1kpGqPIH8nmpUEbCM1XgeqJSdEzMb\nzhdfhLQ0uT9tmvxRBwfD4MEy74UXzIZ31Sr5eeGC82s8v/x5xqwbw7WEa4Bci7djtY52RyyHBIaY\n9i++ddHmuEJRGNANb79+8vf255+y52npUvmCW7OmDDawZg3cfz/cc4/cDw6Wo0WFkOUuXYKxY2Vd\nN2/KrumMDPfppTAely9L+zBvnrQF48fDRx+5W6rs4aoBV5ktmd0m4zOfzqdzZ3N661agoXnk2Tfb\n4JumFic0lB97Aa0xiDs/7BdegOhK4NsEmj8Pw4ebh5Pb5RSsPrGaztU7M379eMa1H+ewaIuKLQgN\nDKV8SD4F9CxAoqKiDDsKUunegZQU+WfUr59sDYCc7wiwaRO0bi33O3WSm47l8miWrsRWrcz7JWSU\nVSZMsL721q3W5QoSdc87uFsMl/H443DmDNStK1/oJk+2dYXs2AHNmkVRtWoHU/SqcY7/2j0OVxjf\nC6nvi9sAACAASURBVIDluMhKd/JsmPvFWP5ZG4y/P4SU8CfBvwQ+tcqy6QrE+16B4kDpO3GQr93p\ntb6Tbl79Ctu2Qtm7y5JYAb5feoX0YLgUVJZ3ZsD99WX5snfL8lf2W/V68/RnTzOho/yniE6KNjnq\n9QdWT0c9E4WP5uPwuDeld+/e7VHyFGR6951+J0+RpyDTQkD//lEsXAjQgb/+go8/juLff2H4cFk+\nJSWKqCj75/ftC889J9Mgjw8ZEsXGjeby16+bj7/7rqy/VSvYubMDrVqp510977lPJybCkiUyvX27\nPF6pUhRdupjL16kTxZEjAPDvv54jv75/OjujiIUQWW5AVWCfg2Pdgd/u7N8LbHFQTvj4yGiZDRsK\nIYQQ06YJ8fLLQiQn61E0zVvRokKcPSvE998LsXOnECkpMv+HH4SJmzfN5aVD2byvM2TFEMF4BOMR\nYR+FiVpf1BIKRWFj7VohSpUS4tVXrX9HDz0kRI0aQmzfbs5btizr+jp0MJd/8UXb4+np5uOLFwtx\n44YQ770nRLdu8lhWnDwpxKBBQly/nnNdFd5BWpq8zxkZ2T9n/34hFiyQz1VwsK1dEEKIc+fM6YED\n80d2VyFNrAO76uiAqQAsBC4CKUjf7nPAYGCwRZn/A44De4B7HNRj+sLatRNi/nz5Z/Gf/5h/yI0b\nm79UPz9bRV59VYjEROu8o0dl+Vu37Bvf2Nux4viN4yYDPGrtKFd8pwqFR7BhgxDPPmv7J9WunRDb\ntglx+rRMd+smPwcNyn7dIMR99zk+vm2bLPPjjzI9cKBMDx3qvN6oKLOc33wjxIED2ZdJ4R1Y/h8v\nXizExo1C/Pyz83PmzTOf88gjMi8jQ4jZs835b71l/Zx365b/uuSFPBlfV22Wxtdye/VVXUghli61\nfcvJDp06WZ+3davM19+41q1bJw5fOywYjzh762yOvjxvZt26de4WwW0Udt2XLhXir7/Mz3z16kJc\nvSrEpUtCrFy5zlTOsnfo/vtzdo2dO4W4eNF5mY0bZa+UEEKsXy+v89JL8jMtTYjUVOvymzfb/gfk\n5LfujMJ+z53haboPHpyz+/zjj/J4ixby89dfzceSk4X4/XchNM1cT2ysEP/9rxBff70u33XJC86M\nb4GGlwwMtM3791/zftmy0Lt3zuu9kWmBoQ4d5BQmHwvtqgTXhvGCcN9MYXsUCi9j82bo1cs8SCo8\nHPbskXFqy5WTI5J1wsLM+y+/nLPrNGmS9ULi990n51UCtGkjZYqPl+nly+Wx48dleswYuVwbwCOP\nQNu2OZNHkTf0mST5Tb9+MlLaW2/JgX2WJCZapz/4QEafeuIJmd66VZrXXr3MZQICZFQ1cWcYb2oq\nhITAO+9A7dr5p0e+48gqu3oDRLNmtq3UAQP0NwTpHxBCiFOnhLh2LftvFwEB8vzixW3ftNLShEhI\nECIoSKa//DKn7y4Kheewf798ju+5R4jSpYVISsr6nLNnhbh8Of9lE0KITz6x/Q3+/bcQ+/aZ0++/\nL8smJkr/M0h/sSJ/0f8nr161zl+2TP5P5hXLXhYQYscOIQ4flvt16ghRtaoQM2fKsnFxQtSrZ11e\n72p2RM+eruslKSjwlG7nlBTZDVWmjLzyokWyS0EIIfbsyb2CpUubH6rXXrO+oV26WKdbtcr9dRQK\nd2LpR3PFn2V+MHOmrfEdMcI6bdmlmJHh2q5no5OWJo1sVJTcT0uTA5gyu/T07/t//5P7RYvKF6e8\noNf70UfW9/PSJfn5ww/SwB4/LsSsWbbyZDVQ78wZITysdz1LPMb46sTGCjFsmOsUnDBBiCeesFQ4\n87ZOgPkte8IEx3UlJ2dvtKY34Gl+oILEk3VPT5dGZ/hwIZ5+OuvyGRlCbNlifp7373de3p26r1hh\nljMoyPxirG9+fkIcOWJ9zvDhwmqsRm7x5Hue3+i6t2pl/X0PG2bev+suIdq2NaeTkoSIjDSnixfP\n/fWrV5d1XLki0/o4AEsuX7b33yx7I9euzd11Pf2eOzO+bllSMCQEPv/cdfWNGsWdOY2SVavgtdes\ny8ycCc2ayf3MfgdLOnWChx92nWwKY5I50pMQMtCFpsmYyj4+MnDAvHnO6/nrLzlW4t57ZXrlSqhf\nP39kdgUPPCA/b92CuDgZKUtn5Ejpr6tVy/ocPVDHkiXmvI8/lul16/JX3sJCaqr8j9u8Gbp1M+dP\nnWr+Pxs+HP75R4ZjBBmhTPfHg9l3n1P27pUxv8PCzNHT7NVVtqxt3o8/yrEI+lgAQ+HIKrt6ww39\nSpZvV9HRMm/SJCFefz3rc/7+u2BkVBQ++vSRz1DfvnKahb23fRBizhxh8ncOHizPvXlTjlFISRFi\n927r8v/841a1ckXmufiOGDxY+rETE4WYPNla75zMEzUill33IF1769ZZp48etW6NlixpPv7zz3La\nWHCwEOfP27/GzZv2pwrdvi1MPt3sUK1a9p6HwgKe1u1cUNj709qwQeatWmXdvZyYKET//tYPsUKR\nHXbtkga0TRsZ1MJySoQ9P9u+fUIsXy73u3Y1H9MDyehdhPp+VtN9PJ3s/J4yG1zL7eWXC0ZObyQj\nQ04h07+rt94yHwMhwsPtn/fGG+Zz9HnW998vxOrV9stPm2Z9H2NjZbCLIkVkXmxs9mUePlyOAzAC\nhjW+y5fLuZCWfgHL6Cj//ivzLEdiWm6rVhW4yC7F0/0h+YkrdI+Pl4M8dBy1wKpWtX12pk2T/k2Q\nz2FCghzhmRl9cIq9rXJl2YrOacvP0+57fHzWZfQWlOU2apR5/6GHsq7D0/TObzIyhChRQn4/Eyeu\nszler57jCFBpaUKMHCkDWOj06iXEkiX2y9eubb4XmZ/Zv/7Kuy65xdPvuTPj6xafb0HRs6fZB6VT\nsaJ5//Zt+blihTlv1Ci5aHiHDvDQQ9C9O8TE5LekioIgJ/Mcf/5ZruJTpYpc1efiRemnrVdPLnGp\n88MPcPq09LclJMilzH77TY45SE2Vq6707AnFipmX4rPk3XflQgZ9+5rzGt5ZUOToUVi82HrZTW/E\nct6xIwID5bxinVdftV60QV/FzOgkJUmTB/J5jI6WK0rZW8xi716YM8d+Pb6+MHEiPPusOS80VPrp\n7aHHUQY5VkHngQds/2MV2cSRVXb1hgf140ZHy9F5K1bItB6+zLLLJfPb3XvvSR+ewrNJS5P3VZ/e\nIIRsef3+u7yPJ044PvfWLdklpocsdbZdvCinTEDOI0fZ49dfZRdeUpKcl7t7d97r9DYSEuT3+csv\n5qlUlqO8c9K16WmkpAjx1VdyClBeANnanTtX7h886Br5hBDilVdkj83x43JczLx58jfRt6/1s693\nNYP1tDGFLRi129kZrVpJv4cQ8kcB8k9PZ8cO+3+6madJKDyHs2fN96l7d/nHPWmS7T0UwjpGeFyc\nrZ/W11dOO8vIMM9d/fdfIZo3l/sTJpjLeuqcW29k4ULbqX769/zOO+6RyRW8/rpZj507c1dH5oFV\nY8e6VsaRI21/K2+/bd7fvVuIn36S+4MGyc+8vkwUdgxvfO35BfQHavp06VtzNKjD3soao0dn/9ru\nHKnp6f4QV6HHEtY3aXDX2dw3fZ63vu3bJ4MM6JF/wLxIQeaVfywNbLly5vJLlxasrtmhsN13/bu2\nHExkD0/UO/McbRDi009zXs/Eibb/Q3pcfCFco/v06bbXeOIJIZo1MwfgOHlS5k+e7BnxEDzxnlvi\nzPgWap+vM2rWlJ8vvwznzln73Cy5ehVmz5b7+pzDzAuIWyIEDBwIkZHSN+jjI/2FlgwcKP14y5fn\nTQdvQAhIT5ffQXq62c/uKg4dkv5WnT/+kH7ULl3MeSkpcPiwnO/41FPm/LfflgvKp6TIeoSQ91oI\n27nevr7mfd3/FR4OnTu7Vh+FLaGh8tMbfy8PPSTnaJcsKccANGsmn7tbt7Jfx5Ej8N57cn/7dhkP\n+eWXYcQI18pqb0zEjz/K+Pv6/6M+V7duXevY+Ypc4Mgqu3rDw7qdV62yfsPLDpbdPvbe+mJibN8c\nQY461ElNtW19FVZiY4WpCzfzdx0ZKbviLNdzTU+X3fqbN1v7bO1x9Kj8nDpViLAwc5hSS3bvth8N\nassWISpWtJZJzSX1XEaPztnv1B3orVvLSE0nTsi8xx4zz7HVe2n05zcrLHtrsopsllemTDFfS4+F\nn/n/LiNDRmXLSex9I4PRu53tkdl/khNAiC++sM1//31bw/vZZ+b9Y8fM05q++86cP2+ea3TyJM6c\nsf0uIiLk58GD1vkbN8pz3n3X+T3J+P/27j1KivLM4/j3AVE0ixIXEkVREvFGRFFkSCQKXgHX1Wi8\nodGoGGEN2Wg2hBPFS0S8BKMssDERjBoN4roGzdmoKIkYiCtmQVEiKmQZoxgRg+IFUGCe/eOpont6\nZnqG6e6qrn6fzzlz+lbT8/6mu+vtqvfWoDp6tOoJJ9jj8+dbG+yPf7zt5Zs82Z5j5szScrpkVHPl\nu2BBrnyjR+fuHzBAdeTIxtuedZZtd2ULy4qPH6/6+ON2PV7nHJIZ65y/bu6aNY2Hern2Cb7ybald\noL2V7+mnN12BY/Vqe57rr8/NVfvii03/zs47W5vhli02p2n+m/2dd9q2Sk1bpdkeEle0t9xindve\ne8/uj/PW1anut1/udv46r/Gi7N2722Lw69ZZB6nmzipA8yv2VHtbUCXVYva2fE6Tyt3QYO2jEyY0\nXjRiwgTrFNbQYF+od9qp6QQpJ51k2152mfUjqKvLjZON17SNc86YYdfvvLP1MpUje9zJdOBAO0O3\ndm31V77V/l73yreFF2jDBvswjBu3bc/3xBPWO7ahIbfEG1gX/PzTM/nyK4vhw5ven38kXC5pvTHj\no8rly5s+FmccMcL+R7Nn5+4788zc6eO4BzqonnxyruNbPLzn6quL/7+q/UNZSbWYfcKE9Cvf3Xe3\nWfBGjmz6BXDlytxp206dWn5vvv56rlNf/HPZZU2nIb3/fru87762la1S2UeOtH1dtar293rwlW+5\nvfpqriIYNarx0VxL8udaLRxqkH96B3Irg2TN5s22Mg3kpk9szu23N66YjzjCfuf55xtvd/PNjf8v\nhZV5fb3q00+Xr/yuem3caJVaGj1sV63KjUHO/xk61IbnrFhh2+WPD991V/u95lxzTdPnin/iz0/8\ns25dYjGb9emn6Zchy7zyrYATT7Sp2PI/KMXWCm5osEq7uaPBeOwc2LqaAwdWrtyVcMMNqpMm5ZYV\na89L3dJONd6h9ehRWhld9nXr1npHvHK7997mK8l4oZZCxx9vj193XcvPmT+HQGHHvw8/zHUw22ef\nymRyySlW+QbRWXzevHllf86+feFXv7LrTzwBzz/feFnDQiK2lFrv3k0fO+MMG7YCtozawoXw17+W\nXsZK5AZYvNiWZezd23JdcQWMHWvLip1/Pqxate3P2dKwhX33tf/Ha69t2/NVKnsW1Gr27t3h3Xdb\nfrwSuWfOzF2fMiV3Pf68Fvrtb+G88+zz0JLDDrOqFmz5vx12sOsbNtgUpFdeabc//rjt5azV17w1\nWc69XdoFyKqDDrI1R3v0KM9Yz3fftYps0ya7vffeVuHE45GrwaZNsP32Te/v2zc33/E995T/79bV\nlf85XfZ07gyffJLM3/r9721+6WXL4Mkn7TN+2GFWsXbv3vLvdeoEv/xl2/5GXAHPmmW5One22507\n2/wC3bqVlsFVN9H4HVDpP2St9on8rSQsXgz9+9s310cfLe9zxxPpP/NM8xOmV9r118OwYbBokU0S\nsOeeVvFed13jCUamTrVJ1ffaq22T5ztXiro6uOwyOOEEWwi+b9/K/J1+/WDJktxtVZvg46STKjOx\nxOOP29Hv0UeX/7ldukQEVW12aRSvfNvp9dehVy+480646KLyPve77+a+XS9ZklvlJgk33JA77dWc\nO+6wI4DDD4fp0+Hii5Mrmwtb/KV06FCYMyd35FhOy5bZylWxfv2sScm59ihW+XqbbzvtsotdVmJ6\nwW7d4Gtfs+uHHNL+52lP7mIVb1zZ9u9vy5iNHNn+slValtuCSlXr2efMaf7+cuTu0wf22ceuL1oE\n8+eX/JSJqPXXvCVZzu1tvu0Uzzfbo0dlnn/mTFsDNt9tt8GMGfDnP1fmbz78sF2uXGmdyE47Dbp0\ngVtvtXVD849yu3atTBmcS8tHH9nlM89Yh6pOndItj6ttftq5iu2/v3W6iv9t8Wm3jz9uWjEDrF4N\nEyda22xcOX70UfOLuOdbvz7XZvulL8HSpeUpv3PlJHkn77bbLtc5EewzEvfDaKsrr7RmljFjrP/C\njTdaR6snnyxfmV3Ygj/tnFVxW1N9feP7ly9vuu3ll1vHqKlT7egYYMcd7ci12NCfVasad5aaO7ek\nIjuXiM2bbWgOQEMDzJ5t/RDio9diDjgATjwR7rrLbk+bZhX7736Xa+5xrtKCqHyz2i6w44526uvb\n38596z/7bOsEMnu23W5osJ3G5Mm5JcHGjrUd0caN84DcEngxVfs9sOXOwMbq/uIXsNtulc2UlKy+\n5uUQSvYRI+Dqq225x1tvBZi3dchbMa++Co89Zj2m7747d/+CBZXrQV1pobzmhbKcO4jKN6tE7LRY\n/lCmtWvt8rTT7HLiRDjuuNzjDzxgl4sW2eXo0XDKKY2fd9Ys22E98wy8+aZdnzgRLrywMjmcK4fp\n0+2oNfbII7a2LcAf/2iXra2Tu2VL49tf/Wqug9Unn9g6tc4lIYjKd8iQIWkXod123z13vWdPGDy4\n8eNXX5273qePLXr97LM2UF9kCIMG2am4+BQd5CbEGDTILsu9wH01yPJrXqpazX7xxfYlsWVDmDbN\nmlEmT2766Btv2NFybO5cq3hffTX3BbfYBBrVrFZf89ZkOXcQlW+W5Ve+N95op4fjivTss+3yuOPs\nNHLcUWrgQKtwN22CU0+1+3bayX7/gw9gzZrcc86YYZ1XnMuCXr2av79rVxg3zirR9eutD0S+hgab\nDObBB+22qk0QA3bmZ/jwyowbdq4lQVS+WW4XyK9846n14mno4lPMt9xip6jze4N27Ajz589r1Jnq\niitsfPKvfw1XXWVzU1fzWN1SZPk1L1UtZ+/bF773Pbj99tx9CxfauPOuXec12nbdOrvcuLFxW+6L\nL1a+nEmr5de8mCznDqLyzbJ4HPHkybkj3UKtzYBV+I1+7VrrxHXOOaWXz7kkdeoEP/mJTfUYGzDA\nLuPOg7EnnrDLgw6Cl1+265MmZbdTlastrY7zFZFhwGSgIzB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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5c6d4adf90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sp500[['Market', 'Strategy']].cumsum().apply(np.exp).plot(grid=True,\n",
    "                                                    figsize=(8, 5))\n",
    "# tag: sp500_wealth\n",
    "# title: The S&P 500 index vs. investor's wealth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further Reading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>\n",
    "\n",
    "<a href=\"http://www.pythonquants.com\" target=\"_blank\">www.pythonquants.com</a> | <a href=\"http://twitter.com/dyjh\" target=\"_blank\">@dyjh</a>\n",
    "\n",
    "<a href=\"mailto:analytics@pythonquants.com\">analytics@pythonquants.com</a>\n",
    "\n",
    "**Python Quant Platform** |\n",
    "<a href=\"http://oreilly.quant-platform.com\">http://oreilly.quant-platform.com</a>\n",
    "\n",
    "**Derivatives Analytics with Python** |\n",
    "<a href=\"http://www.derivatives-analytics-with-python.com\" target=\"_blank\">Derivatives Analytics @ Wiley Finance</a>\n",
    "\n",
    "**Python for Finance** |\n",
    "<a href=\"http://shop.oreilly.com/product/0636920032441.do\" target=\"_blank\">Python for Finance @ O'Reilly</a>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python2",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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